14 citations · 98 across the 27 of their papers we have counts for
27 papers
NaturalL2S: End-to-End High-quality Multispeaker Lip-to-Speech Synthesis with Differential Digital Signal Processing
Yifan Liang, Fangkun Liu, Andong Li +2
Recent advancements in visual speech recognition (VSR) have promoted progress in lip-to-speech synthesis, where pre-trained VSR models enhance the intelligibility of synthesized sp…
Neural Vocoders as Speech Enhancers
Andong Li, Zhihang Sun, Fengyuan Hao +2
Speech enhancement (SE) and neural vocoding are traditionally viewed as separate tasks. In this work, we observe them under a common thread: the rank behavior of these processes. T…
TaylorBeamixer: Learning Taylor-Inspired All-Neural Multi-Channel Speech Enhancement from Beam-Space Dictionary Perspective
Andong Li, Guochen Yu, Wenzhe Liu +2
Despite the promising performance of existing frame-wise all-neural beamformers in the speech enhancement field, it remains unclear what the underlying mechanism exists. In this pa…
A General Unfolding Speech Enhancement Method Motivated by Taylor's Theorem
Andong Li, Guochen Yu, Chengshi Zheng +2
While deep neural networks have facilitated significant advancements in the field of speech enhancement, most existing methods are developed following either empirical or relativel…
Taylor, Can You Hear Me Now? A Taylor-Unfolding Framework for Monaural Speech Enhancement
Andong Li, Shan You, Guochen Yu +2
While the deep learning techniques promote the rapid development of the speech enhancement (SE) community, most schemes only pursue the performance in a black-box manner and lack a…
TaylorBeamformer: Learning All-Neural Beamformer for Multi-Channel Speech Enhancement from Taylor's Approximation Theory
Andong Li, Guochen Yu, Chengshi Zheng +1
While existing end-to-end beamformers achieve impressive performance in various front-end speech processing tasks, they usually encapsulate the whole process into a black box and t…
MDNet: Learning Monaural Speech Enhancement from Deep Prior Gradient
Andong Li, Chengshi Zheng, Ziyang Zhang +1
While traditional statistical signal processing model-based methods can derive the optimal estimators relying on specific statistical assumptions, current learning-based methods fu…
Low-latency Monaural Speech Enhancement with Deep Filter-bank Equalizer
Chengshi Zheng, Wenzhe Liu, Andong Li +2
It is highly desirable that speech enhancement algorithms can achieve good performance while keeping low latency for many applications, such as digital hearing aids, acoustically t…
A Neural Beam Filter for Real-time Multi-channel Speech Enhancement
Wenzhe Liu, Andong Li, Chengshi Zheng +1
Most deep learning-based multi-channel speech enhancement methods focus on designing a set of beamforming coefficients to directly filter the low signal-to-noise ratio signals rece…
A deep complex multi-frame filtering network for stereophonic acoustic echo cancellation
Linjuan Cheng, Chengshi Zheng, Andong Li +3
In hands-free communication system, the coupling between loudspeaker and microphone generates echo signal, which can severely influence the quality of communication. Meanwhile, var…
EmotionBox: a music-element-driven emotional music generation system using Recurrent Neural Network
Kaitong Zheng, Ruijie Meng, Chengshi Zheng +4
With the development of deep neural networks, automatic music composition has made great progress. Although emotional music can evoke listeners' different emotions and it is import…
Noise-robust blind reverberation time estimation using noise-aware time-frequency masking
Kaitong Zheng, Chengshi Zheng, Jinqiu Sang +2
The reverberation time is one of the most important parameters used to characterize the acoustic property of an enclosure. In real-world scenarios, it is much more convenient to es…
Embedding and Beamforming: All-neural Causal Beamformer for Multichannel Speech Enhancement
Andong Li, Wenzhe Liu, Chengshi Zheng +1
The spatial covariance matrix has been considered to be significant for beamformers. Standing upon the intersection of traditional beamformers and deep neural networks, we propose…
A Simultaneous Denoising and Dereverberation Framework with Target Decoupling
Andong Li, Wenzhe Liu, Xiaoxue Luo +3
Background noise and room reverberation are regarded as two major factors to degrade the subjective speech quality. In this paper, we propose an integrated framework to address sim…
Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement
Andong Li, Chengshi Zheng, Lu Zhang +1
The capability of the human to pay attention to both coarse and fine-grained regions has been applied to computer vision tasks. Motivated by that, we propose a collaborative learni…
Learning to Inference with Early Exit in the Progressive Speech Enhancement
Andong Li, Chengshi Zheng, Lu Zhang +1
In real scenarios, it is often necessary and significant to control the inference speed of speech enhancement systems under different conditions. To this end, we propose a stage-wi…
ICASSP 2021 Deep Noise Suppression Challenge: Decoupling Magnitude and Phase Optimization with a Two-Stage Deep Network
Andong Li, Wenzhe Liu, Xiaoxue Luo +2
It remains a tough challenge to recover the speech signals contaminated by various noises under real acoustic environments. To this end, we propose a novel system for denoising in…
A Robust Maximum Likelihood Distortionless Response Beamformer based on a Complex Generalized Gaussian Distribution
Weixin Meng, Chengshi Zheng, Xiaodong Li
For multichannel speech enhancement, this letter derives a robust maximum likelihood distortionless response beamformer by modeling speech sparse priors with a complex generalized…
Two Heads Are Better Than One: A Two-Stage Approach for Monaural Noise Reduction in the Complex Domain
Andong Li, Chengshi Zheng, Renhua Peng +1
In low signal-to-noise ratio conditions, it is difficult to effectively recover the magnitude and phase information simultaneously. To address this problem, this paper proposes a t…
Distributed Node-Specific Block-Diagonal LCMV Beamforming in Wireless Acoustic Sensor Networks
Xinwei Guo, Minmin Yuan, Chengshi Zheng +1
This paper derives the analytical solution of a novel distributed node-specific block-diagonal linearly constrained minimum variance beamformer from the centralized linearly constr…
Dynamic Attention Based Generative Adversarial Network with Phase Post-Processing for Speech Enhancement
Andong Li, Chengshi Zheng, Renhua Peng +2
The generative adversarial networks (GANs) have facilitated the development of speech enhancement recently. Nevertheless, the performance advantage is still limited when compared w…
The IOA System for Deep Noise Suppression Challenge using a Framework Combining Dynamic Attention and Recursive Learning
Andong Li, Chengshi Zheng, Renhua Peng +2
This technical report describes our system that is submitted to the Deep Noise Suppression Challenge and presents the results for the non-real-time track. To refine the estimation…
A Recursive Network with Dynamic Attention for Monaural Speech Enhancement
Andong Li, Chengshi Zheng, Cunhang Fan +2
A person tends to generate dynamic attention towards speech under complicated environments. Based on this phenomenon, we propose a framework combining dynamic attention and recursi…
A Time-domain Monaural Speech Enhancement with Feedback Learning
Andong Li, Chengshi Zheng, Linjuan Cheng +2
In this paper, we propose a type of neural network with feedback learning in the time domain called FTNet for monaural speech enhancement, where the proposed network consists of th…